Sequence Labeling

In this assignment, you will work on the MeasEval shared task that was part of SemEval-2021. The goal of MeasEval is the extraction of counts, measurements, and related context from scientific documents. The task is a complex problem that involves solving a number of steps that range from identifying quantities and units of measurement to identify relationships between them. For this assignment, you will focus only on the Quantity recognition step:

• Given a paragraph from a scientific text, identify all spans containing quantities like 12 kg. This problem can be approached as a Sequence Labeling task.

You will develop a Recurrent Neural Network with Keras, a high-level Deep Learning API written in Python that provides a user-friendly interface for the TensorFlow library, one of the most popular low-level Deep Learning frameworks. You will use the following objects and functions:

import pandas as pd
import numpy as np
from tensorflow.keras.utils import set_random_seed
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, TimeDistributed
from tensorflow.keras.initializers import Constant
from sklearn.metrics import classification_report

When working with Neural Networks, there are a large number of random operations such as initializing the weights of the network, shuffling the data for training, or choosing samples. This causes that different training runs of the same model can lead to different results. To ensure reproducibility, i.e. obtaining the same results in the different runs, the random number generator must be initialized with a fixed value known as seed. In Keras, this can be done as follows:

seed = 42
set_random_seed(seed)

When developing a model, if the results you get are not as expected, try re-initializing the seed by running the cell above before compiling and training the model.

Note! With models as complex as Neural Networks, reproducibility is susceptible to factors such as software versions or the hardware on which the models are run. Even with seed initialization, there may be slight differences in the results.

Working with Neural Networks also involves defining a number of hyperparameters that set the configuration of the model. Finding the appropriate hyperparameter values requires training the model with different combinations and testing them on the development set. This hyperparameter tuning is a costly process that needs multiple rounds of experimentation. However, for this assignments, you will use the following values:

maxlen = 130  # Maximum length of the input sequence accepted by the model
epochs = 6  # Number of epochs to train the model
batch_size = 64  # Number of examples used per gradient update
embedding_dim = 300  # Dimension of the embeddings
rnn_units = 256  # Number of units per RNN layer

Training a Deep Learning model with a large train set can be a time-consuming process, as the model needs to iterate over the entire set multiple times, often requiring significant computational resources. During the implementation of the model, it is often a good practice to use only a subset of the training data. This allows a faster debugging of the code. Set the shrink_dataset variable as True when a faster training is required and set it as False to train the model on the whole train set:

shrink_dataset = False

Although the value of this variable does not affect the tests that will evaluate your code, the output examples distributed throughout this notebook are based on a shrink_dataset variable set as False.

The train set for the assignment consists of 248 articles with 1366 sentences in total. The test set contains 136 articles with 848 sentences. A development set with 65 documents and 459 sentences is also provided. The dataset is annotated at the token level following a BIO schema with 3 labels: B-Quantity, I-Quantity and O. The dataset can be loaded into three DataFrames as follows:

def load_data(data_path, shrink_dataset, seed):
    data = pd.read_csv(data_path, sep="\t", encoding="utf8").dropna()
    if shrink_dataset:
        sample = data[["docId",  "sentId"]].drop_duplicates().sample(frac=0.2, random_state=seed)
        data = pd.merge(data, sample, on=["docId", "sentId"])
    return data

train_data = load_data("data/train.tsv", shrink_dataset, seed)
dev_data = load_data("data/trial.tsv", shrink_dataset, seed)
test_data = load_data("data/eval.tsv", shrink_dataset, seed)
train_data[(train_data.docId == "S0378383912000130-3601") & (train_data.sentId == 3)]

	docId	sentId	word	lemma	label
22276	S0378383912000130-3601	3	The	the	O
22277	S0378383912000130-3601	3	experiments	experiment	O
22278	S0378383912000130-3601	3	involved	involve	O
22279	S0378383912000130-3601	3	two	two	B-Quantity
22280	S0378383912000130-3601	3	beach	beach	I-Quantity
22281	S0378383912000130-3601	3	materials	material	I-Quantity
22282	S0378383912000130-3601	3	with	with	O
22283	S0378383912000130-3601	3	nominal	nominal	O
22284	S0378383912000130-3601	3	sediment	sediment	O
22285	S0378383912000130-3601	3	diameters	diameter	O
22286	S0378383912000130-3601	3	of	of	O
22287	S0378383912000130-3601	3	1.5	1.5	B-Quantity
22288	S0378383912000130-3601	3	mm	mm	I-Quantity
22289	S0378383912000130-3601	3	and	and	I-Quantity
22290	S0378383912000130-3601	3	8.5	8.5	I-Quantity
22291	S0378383912000130-3601	3	mm	mm	I-Quantity
22292	S0378383912000130-3601	3	.	.	O

The DataFrames created include the lemmatization of words in the lemma columns. You will use the lemmas as the input of the model.

Data Pre-processing

In this assignment, you will have to implement some steps to pre-process and obtain a representation of the data. You will implement a model with an Embedding lookup table as the input layer, so the tokens of the input sentences should be represented as indexes. The target labels should also be represented in similar way. Besides, as one would expect, the sentences in the MeasEval dataset have different lengths. However, the input for a Deep Learning model is a batch of examples (in this case, sentences) in the form of a single tensor which requires that all examples in the batch must have the same length. Therefore, the sentences should be padded or truncated to a specific length.

Note! For this particular task, the maxlen value provided to you guarantees that padding is sufficient to make all sentences the same length without the need for truncation.

This first of these pre-processing steps will be to obtain both a vocabulary and the set of labels from the train set. The vocabulary should be the list of unique lemmas and must include the special tokens [PAD], that will be used for padding the sequences, and [UNK], that will be used to represent out-of-vocabulary words. Along with the vocabulary and the label set, you will also have to build a dictionary mapping each lemma to its position in the vocabulary and a dictionary mapping each label to its position in the label set. These dictionaries will be used later to obtain the representation of the input and output of the model. The text is already tokenized and lemmatized which will help in this task.

You must complete the code for the get_vocabulary function that takes as input the DataFrame containing the train set. The function should create a list with the all the unique lemmas and include the special tokens [PAD] and [UNK] in the first two positions. Similarly, the function should create a list with the unique labels with the special token [PAD] in the first position. The pandas library provides some functions that may help you. Along with those lists, get_vocabulary should return the dictionaries mapping the lemmas and the labels to their corresponding positions. In total, the vocabulary and the label set should have 5508 and 4 items respectively:

Vocabulary size: 5508
Vocabulary first 5 lemmas: ['[PAD]', '[UNK]', 'datum', 'be', 'draw']
Vocabulary dictionary: {'[PAD]': 0, '[UNK]': 1, 'datum': 2, 'be': 3, 'draw': 4}

Labels size: 4
Labels: ['[PAD]', 'O', 'B-Quantity', 'I-Quantity']
Labels dictionary: {'[PAD]': 0, 'O': 1, 'B-Quantity': 2, 'I-Quantity': 3}

test_get_vocabulary = 3 Marks

def get_vocabulary(train_data):
    
    # Get unique lemmas from the 'word' column
    unique_lemmas = train_data['lemma'].unique()
    
    # Add special tokens [PAD] and [UNK] to the beginning of the vocabulary
    vocab = ['[PAD]', '[UNK]'] + unique_lemmas.tolist()
    
   
    # Create lemma to index mapping
    word2idx = {lemma: idx for idx, lemma in enumerate(vocab)}
    
    # Define the label set explicitly
    labels = ['[PAD]', 'O', 'B-Quantity', 'I-Quantity']
    
    # Create label to index mapping
    label2idx = {label: idx for idx, label in enumerate(labels)}
    
    return vocab, word2idx, labels, label2idx

vocab, word2idx, labels, label2idx = get_vocabulary(train_data)
vocab_size = len(vocab)
label_size = len(labels)
print(f"Vocabulary size: {vocab_size}")
print(f"Vocabulary first 5 words: {vocab[:5]}")
print(f"Vocabulary dictionary: { {w: word2idx[w] for w in vocab[:5]}}")
print("")
print(f"Labels size: {label_size}")
print(f"Labels: {labels}")
print(f"Labels dictionary: {label2idx}")

Vocabulary size: 5508
Vocabulary first 5 words: ['[PAD]', '[UNK]', 'datum', 'be', 'draw']
Vocabulary dictionary: {'[PAD]': 0, '[UNK]': 1, 'datum': 2, 'be': 3, 'draw': 4}

Labels size: 4
Labels: ['[PAD]', 'O', 'B-Quantity', 'I-Quantity']
Labels dictionary: {'[PAD]': 0, 'O': 1, 'B-Quantity': 2, 'I-Quantity': 3}

Since the Quantity recognition task is a Sequence Labeling problem, the input for the model must be the sequence of lemmas in the sentence and the output the sequence of labels. Therefore, the train, development and test DataFrames must be reformated by aggregating the data corresponding to each sentence. The integrate_sentences will do this for you. The output of integrate_sentences is a DataFrame with a row for each sentence and the columns lemmas and labels that contain the list of lemmas and the list of labels of the sentences respectively.

def integrate_sentences(data):
    agg_func = lambda s: [s['lemma'].values.tolist(), s['label'].values.tolist()]
    data = data.groupby(["docId", "sentId"], sort=False).apply(agg_func).reset_index().rename(columns={0: 'lemmas_labels'})
    data['lemmas'] = data.apply(lambda x: x['lemmas_labels'][0], axis=1)
    data['labels'] = data.apply(lambda x: x['lemmas_labels'][1], axis=1)
    data = data.drop(columns="lemmas_labels")
    return data

train_examples = integrate_sentences(train_data)
dev_examples = integrate_sentences(dev_data)
test_examples = integrate_sentences(test_data)
pd.set_option('display.max_colwidth', None)
train_examples[(train_examples.docId == "S0378383912000130-3601") & (train_examples.sentId == 3)]

	docId	sentId	lemmas	labels
767	S0378383912000130-3601	3	[the, experiment, involve, two, beach, material, with, nominal, sediment, diameter, of, 1.5, mm, and, 8.5, mm, .]	[O, O, O, B-Quantity, I-Quantity, I-Quantity, O, O, O, O, O, B-Quantity, I-Quantity, I-Quantity, I-Quantity, I-Quantity, O]

The dataset is now ready for you to get the numerical representation of both input and output. You must perform two steps to process the sequence of lemmas and the sequence of labels:

1. For each sentence, translate each lemma or label to its corresponding index using the word2idx and label2idx dictionaries. In case the lemma is not found in word2idx, use the index of the [UNK] token instead.
2. Pad both the sequences of lemmas and the sequences of labels to the same length as defined by the maxlen variable. For this, you should use the pad_sequences function with its default padding strategy. This function uses 0 as the default padding value which corresponds to the index of the [PAD] token in the vocabulary.

You must complete the code for the format_examples function. This function takes as input a DataFrame in the format returned by integrate_sentences, the word2idx and label2idx dictionaries, and the maxlen variable. The function must run the steps described above and return a numpy array with the processed lemma sequences and a numpy array with the processed label sequences that will be used as input and output of the model respectively. Applying format_examples to the train, development and test sets should result on 6 arrays with the following shapes:

Shape of train input data : (1366, 130)
Shape of train output data : (1366, 130)
Shape of development input data : (459, 130)
Shape of development output data : (459, 130)
Shape of test input data : (848, 130)
Shape of test output data : (848, 130)

test_format_examples = 3 Marks

def format_examples(data, word2idx, label2idx, maxlen):
    # Convert lemmas and labels to their corresponding indices
    data['lemmas_idx'] = data['lemmas'].apply(lambda lemmas: [word2idx.get(lemma, word2idx['[UNK]']) for lemma in lemmas])
    data['labels_idx'] = data['labels'].apply(lambda labels: [label2idx[label] for label in labels])
    
    # Pad the sequences
    lemmas_padded = pad_sequences(data['lemmas_idx'].tolist(), maxlen=maxlen, padding='post')
    labels_padded = pad_sequences(data['labels_idx'].tolist(), maxlen=maxlen, padding='post')
    
    return np.array(lemmas_padded), np.array(labels_padded)

x_train, y_train = format_examples(train_examples, word2idx, label2idx, maxlen)
x_dev, y_dev = format_examples(dev_examples, word2idx, label2idx, maxlen)
x_test, y_test = format_examples(test_examples, word2idx, label2idx, maxlen)
print("Shape of train input data: ", x_train.shape)
print("Shape of train output data: ", y_train.shape)
print("Shape of development input data: ", x_dev.shape)
print("Shape of development output data: ", y_dev.shape)
print("Shape of train input data: ", x_test.shape)
print("Shape of test output data: ", y_test.shape)

Shape of train input data:  (1366, 130)
Shape of train output data:  (1366, 130)
Shape of development input data:  (459, 130)
Shape of development output data:  (459, 130)
Shape of train input data:  (848, 130)
Shape of test output data:  (848, 130)

Recurrent Neural Network

There are three ways to create a neural network with Kerars: using the Functional API, by Model subclassing or creating a Sequential model. In this assignment, you will use the latter option. A Sequential model is a straightforward approach to build simple neural networks by stacking the layers. You will construct a RNN with the following 3 layers:

1. An Embedding layer with an input dimension equal to the vocabulary size, an embedding dimension defined by embedding_dim and where the length of the input sequences is equal to maxlen. The layer must also mask out the padding values so that they are not considered when computing the loss.
2. A Bidirectional LSTM layer with a number of units determined by rnn_units. Since you are working on Sequence Labeling, the LSTM must return outputs for the full sequence. To make it Bidirectional, the LSTM must be wrapped by a Bidirectional layer.
3. A Dense layer with a number of units equal to the number of labels and a softmax activation function. Since you are working on Sequence Labeling, the Dense layer must be wrapped by a TimeDistributed layer.

You must complete the code for the create_model function. This function takes as input the size of the vocabulary, the number of labels and the maxlen, embedding_dim and rnn_units hyperparameters. The function must create a RNN according to the configuration described above. Read carefully all the linked documentation to learn how to create such a model. Any option not mentioned in the description should be kept with its default value. The summary of the resulting model should look like:

Model: "sequential_1"
__________________________________________________________________________________________
Layer (type)                           Output Shape                        Param #       
==========================================================================================
embedding_1 (Embedding)               (None, 130, 300)                    1652400       
                                                                                         
bidirectional_1 (Bidirectional)       (None, 130, 512)                    1140736       
                                                                                         
time_distributed_1 (TimeDistributed)  (None, 130, 4)                      2052          
                                                                                         
==========================================================================================
Total params: 2,795,188
Trainable params: 2,795,188
Non-trainable params: 0
__________________________________________________________________________________________

Before returning the model, the create_model function should compile it using 'sparse_categorical_crossentropy' as the loss function, 'adam' as the optimizer and 'sparse_categorical_accuracy' as a metric to evaluate the model during training.

test_create_model = 3 Marks

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, TimeDistributed
from tensorflow.keras.optimizers import Adam

def create_model(vocab_size, label_size, maxlen, embedding_dim, rnn_units):
    model = Sequential()
    model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_shape=(maxlen,), mask_zero=True))
    model.add(Bidirectional(LSTM(units=rnn_units, return_sequences=True)))
    model.add(TimeDistributed(Dense(units=label_size, activation='softmax')))
    
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer=Adam(),
                  metrics=['sparse_categorical_accuracy'])
    
    return model

model = create_model(vocab_size, label_size, maxlen, embedding_dim, rnn_units)
model.summary(line_length=90)

C:\Users\Vishal\anaconda3\Lib\site-packages\keras\src\layers\core\embedding.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                          ┃ Output Shape                 ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding (Embedding)                 │ (None, 130, 300)             │       1,652,400 │
├───────────────────────────────────────┼──────────────────────────────┼─────────────────┤
│ bidirectional (Bidirectional)         │ (None, 130, 512)             │       1,140,736 │
├───────────────────────────────────────┼──────────────────────────────┼─────────────────┤
│ time_distributed (TimeDistributed)    │ (None, 130, 4)               │           2,052 │
└───────────────────────────────────────┴──────────────────────────────┴─────────────────┘

 Total params: 2,795,188 (10.66 MB)

 Trainable params: 2,795,188 (10.66 MB)

 Non-trainable params: 0 (0.00 B)

Once the data has been processed and the model has been compiled, you can proceed to train it.

You must complete the train_model function. The function takes as input the model created by create_model, the train input and output obtained by format_examples as well as the development input and output produced by the same function. The function also takes the batch_size and epochs hyperparameters. The function should train the model on the training data using those hyperparameters. During the training, train_model should evaluate the loss and any model metrics on the development data. With shrink_dataset = False, the training will take several minutes.

test_train_model= 4 Marks

def train_model(model, x_train, y_train, x_dev, y_dev, batch_size, epochs):
    history = model.fit(x_train, y_train,
                        batch_size=batch_size,
                        epochs=epochs,
                        validation_data=(x_dev, y_dev))
    return history

train_model(model, x_train, y_train, x_dev, y_dev, batch_size, epochs)

Epoch 1/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 51s 2s/step - loss: 0.8103 - sparse_categorical_accuracy: 0.3085 - val_loss: 0.2376 - val_sparse_categorical_accuracy: 0.2154
Epoch 2/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 32s 1s/step - loss: 0.2280 - sparse_categorical_accuracy: 0.2048 - val_loss: 0.1829 - val_sparse_categorical_accuracy: 0.2161
Epoch 3/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 40s 1s/step - loss: 0.1537 - sparse_categorical_accuracy: 0.2067 - val_loss: 0.1307 - val_sparse_categorical_accuracy: 0.2193
Epoch 4/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 39s 2s/step - loss: 0.0962 - sparse_categorical_accuracy: 0.2107 - val_loss: 0.1096 - val_sparse_categorical_accuracy: 0.2209
Epoch 5/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 43s 2s/step - loss: 0.0687 - sparse_categorical_accuracy: 0.2127 - val_loss: 0.0978 - val_sparse_categorical_accuracy: 0.2215
Epoch 6/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 44s 2s/step - loss: 0.0527 - sparse_categorical_accuracy: 0.2138 - val_loss: 0.0934 - val_sparse_categorical_accuracy: 0.2216

<keras.src.callbacks.history.History at 0x200370f5110>

After training, the model can be used to make predictions on unlabeled data using the predict method.

You must complete the code for the make_predictions function. The functions takes as input the model already trained, the test input data produced by format_examples and the batch_size hyperparameter. The function must run the predict method on the input data using batches of size equal to batch_size. The predict method will return a numpy array with 3 axes: (number of sentences, maxlen, label_size). For each token in each sentence, predict returns a vector with the probabilities predicted for every label. The output of make_predictions must include only the index of the label with the highest probability for each token. For example, if the prediction for one token is the vector [0.04974193, 0.1511916, 0.65180656, 0.14725993], the output for that token should be 2. For this, you can apply the argmax method along the last axis of the numpy array.

test_make_predictions= 3 Marks

import numpy as np

def make_predictions(model, x_test, batch_size):
    predictions = []
    num_samples = len(x_test)
    
    # Iterate over the test data in batches
    for i in range(0, num_samples, batch_size):
        batch_x = x_test[i:i+batch_size]
        batch_pred = model.predict(batch_x, batch_size=batch_size)
        
        # Convert predicted probabilities to label indices
        batch_pred_labels = np.argmax(batch_pred, axis=-1)
        
        predictions.append(batch_pred_labels)
    
    # Stack predictions into a single numpy array
    predictions = np.vstack(predictions)
    
    return predictions

predictions = make_predictions(model, x_test, batch_size)

1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 278ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 271ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 276ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 262ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 270ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 278ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step

Since the predictions are now label indexes, they can be translated to the corresponding label by accessing the labels list. The predictions_to_labels functions iterates over all the sequences in the test set and translates the prediction of each token to the corresponding label. The function skips the padding tokens. The new format of the predictions can be stored in the prediction column of the test DataFrame.

def predictions_to_labels(predictions, x_test, labels):
    pred_labels = []
    for pred_seq, x_seq in zip(predictions, x_test):
        pred_seq_labels = [labels[p] for p, x in zip(pred_seq, x_seq) if x!=0]
        pred_labels.extend(pred_seq_labels)
    return pred_labels

test_data['prediction'] = predictions_to_labels(predictions, x_test, labels)
test_data[(test_data.docId == "S0038071711004354-1624") & (test_data.sentId == 2)]

	docId	sentId	word	lemma	label	prediction
7328	S0038071711004354-1624	2	Approximately	approximately	B-Quantity	B-Quantity
7329	S0038071711004354-1624	2	15	15	I-Quantity	B-Quantity
7330	S0038071711004354-1624	2	min	min	I-Quantity	I-Quantity
7331	S0038071711004354-1624	2	before	before	O	O
7332	S0038071711004354-1624	2	the	the	O	O
7333	S0038071711004354-1624	2	beginning	beginning	O	O
7334	S0038071711004354-1624	2	of	of	O	O
7335	S0038071711004354-1624	2	the	the	O	O
7336	S0038071711004354-1624	2	experiment	experiment	O	O
7337	S0038071711004354-1624	2	,	,	O	O
7338	S0038071711004354-1624	2	the	the	O	O
7339	S0038071711004354-1624	2	aboveground	aboveground	O	O
7340	S0038071711004354-1624	2	vegetation	vegetation	O	O
7341	S0038071711004354-1624	2	was	be	O	O
7342	S0038071711004354-1624	2	removed	remove	O	O
7343	S0038071711004354-1624	2	under	under	O	O
7344	S0038071711004354-1624	2	the	the	O	O
7345	S0038071711004354-1624	2	dual	dual	O	O
7346	S0038071711004354-1624	2	-	-	O	O
7347	S0038071711004354-1624	2	chamber	chamber	O	O
7348	S0038071711004354-1624	2	,	,	O	O
7349	S0038071711004354-1624	2	as	as	O	O
7350	S0038071711004354-1624	2	well	well	O	O
7351	S0038071711004354-1624	2	as	as	O	O
7352	S0038071711004354-1624	2	the	the	O	O
7353	S0038071711004354-1624	2	reference	reference	O	O
7354	S0038071711004354-1624	2	chamber	chamber	O	O
7355	S0038071711004354-1624	2	which	which	O	O
7356	S0038071711004354-1624	2	was	be	O	O
7357	S0038071711004354-1624	2	deployed	deploy	O	O
7358	S0038071711004354-1624	2	at	at	O	O
7359	S0038071711004354-1624	2	30	30	B-Quantity	B-Quantity
7360	S0038071711004354-1624	2	cm	cm	I-Quantity	I-Quantity
7361	S0038071711004354-1624	2	distance	distance	O	O
7362	S0038071711004354-1624	2	from	from	O	O
7363	S0038071711004354-1624	2	the	the	O	O
7364	S0038071711004354-1624	2	dual	dual	O	O
7365	S0038071711004354-1624	2	-	-	O	O
7366	S0038071711004354-1624	2	chamber	chamber	O	O
7367	S0038071711004354-1624	2	.	.	O	O

Although it is not the usual way of evaluating Sequence Labeling tasks, MeasEval uses a metric based on the Reading Comprehension Macro-Averaged F1. This metric measures the amount of overlapping tokens between the predictions and the true labels. In this assignment, we will approximate this metric by evaluating how many tokens belonging to a Quantity are captured by the model. This is done by the evaluate function. For the model trained above, the result of this evaluation should look like:

              precision    recall  f1-score   support

    Quantity       0.85      0.69      0.76      1263

   micro avg       0.85      0.69      0.76      1263
   macro avg       0.85      0.69      0.76      1263
weighted avg       0.85      0.69      0.76      1263

def evaluate(data):
    labels = data.apply(lambda x: x['label'].replace("B-", "").replace("I-", ""), axis=1).values
    predictions = data.apply(lambda x: x['prediction'].replace("B-", "").replace("I-", ""), axis=1).values
    print(classification_report(labels, predictions, labels=["Quantity"]))

evaluate(test_data)

              precision    recall  f1-score   support

    Quantity       0.86      0.75      0.80      1263

   micro avg       0.86      0.75      0.80      1263
   macro avg       0.86      0.75      0.80      1263
weighted avg       0.86      0.75      0.80      1263

Pre-trained Word Embeddings

Initializing neural networks with pre-trained word embeddings has a significant impact on many NLP tasks. In the following exercise, you will experiment whether this is also the case for Quantity recognition using GloVe. You can refer to the following tutorial to learn how to complete this exercise with keras: Using pre-trained word embeddings

First, the load_embeddings function will load GloVe and return a dictionary mapping words to their embeddings.

def load_embeddings(glove_path):
    embedding_index = {}
    with open(glove_path, encoding="utf8") as glove_file:
        for line in glove_file:
            word, coefs = line.split(maxsplit=1)
            coefs = np.fromstring(coefs, "f", sep=" ")
            embedding_index[word] = coefs
    return embedding_index

glove_path = f"glove/glove.6B.{embedding_dim}d.txt"
embedding_index = load_embeddings(glove_path)

To initialize the Embedding layer with the GloVe embeddings, you have to create a matrix with (vocab_size, embedding_dim) dimensions. The i-th row in the matrix corresponds to the i-th lemma in the vocabulary and contains the GloVe embedding for that lemma.

You must complete the code for the create_embedding_matrix function. The function takes the embedding dictionary created by load_embeddings, the vocabulary dictionary, the size of the vocabulary and the embedding_dim hyperparameter. The function should initialize a (vocab_size, embedding_dim) numpy array with zeros and then replace each row with the appropriate GloVe embedding if the corresponding lemma exists in the embedding dictionary. For example, the embedding for "statistic" should exist in the resulting embedding_matrix:

array([ 0.1085    ,  0.82801998,  0.10672   ,  0.0094136 , -0.30441001,
        0.75617999, -0.14704999, -0.15469   , -0.97372001, -0.60413003,
        0.065233  , -0.055324  , -0.094477  ,  0.23502   ,  0.16466001,
        ...

test_create_embedding_matrix= 2 Marks

import numpy as np

def create_embedding_matrix(embedding_index, word2idx, vocab_size, embedding_dim):
    # Initialize the embedding matrix with zeros
    embedding_matrix = np.zeros((vocab_size, embedding_dim))
    
    # Populate the embedding matrix with the appropriate GloVe embeddings
    for word, idx in word2idx.items():
        if idx < vocab_size:
            embedding_vector = embedding_index.get(word)
            if embedding_vector is not None:
                embedding_matrix[idx] = embedding_vector
    
    return embedding_matrix

embedding_matrix = create_embedding_matrix(embedding_index, word2idx, vocab_size, embedding_dim)
embedding_matrix[word2idx["statistic"]]

array([ 0.1085    ,  0.82801998,  0.10672   ,  0.0094136 , -0.30441001,
        0.75617999, -0.14704999, -0.15469   , -0.97372001, -0.60413003,
        0.065233  , -0.055324  , -0.094477  ,  0.23502   ,  0.16466001,
       -0.043116  ,  0.12965   , -0.48658001,  0.14789   , -0.21789999,
        0.53388   ,  0.0022767 , -0.11502   ,  0.50904   ,  0.046882  ,
        0.76059002, -0.15012001,  0.070032  ,  0.12826   , -0.26036999,
        0.030337  , -0.20457   , -0.73719001, -0.41698   , -0.39225999,
        0.10444   , -0.27976999,  0.36368999, -0.53135002, -0.24784   ,
        0.72441   ,  0.24203999, -0.089047  , -0.030367  , -0.39888999,
        0.019355  , -0.66887999, -0.36210999, -0.41176999,  0.59864998,
        0.25565001,  0.38666001, -0.064241  ,  0.22142   , -0.18565001,
        0.32170999,  0.20461001, -0.037658  , -0.18867999,  0.06231   ,
       -0.33331001, -0.065288  ,  0.36129999,  0.069083  , -0.45622   ,
       -0.080719  , -0.19212   ,  0.017178  ,  0.50905001,  0.81054997,
       -0.96237999,  0.57374001, -0.68526   ,  0.075411  , -0.53068   ,
       -0.047891  ,  0.44292   , -0.42081001, -0.28928   ,  0.16065   ,
       -0.30575001,  0.21333   ,  0.62694001, -0.028551  , -0.23056   ,
        0.37834001, -0.06101   , -0.10411   ,  0.04182   ,  0.36056   ,
       -0.22515   , -0.13383999, -0.21155   , -0.37575001, -0.022031  ,
        0.43046999,  0.066295  , -0.03755   ,  0.0179    ,  0.095457  ,
        0.32977   ,  0.3082    ,  0.0081692 , -0.16071001,  0.13176   ,
       -0.51991999, -0.21731   , -0.47982001, -0.28992999,  0.62965   ,
        0.26859   ,  0.093778  , -0.87300003, -0.02335   ,  0.63243002,
        0.40823999,  0.45792001,  0.21115001,  0.03629   , -0.046396  ,
        0.33298999,  0.45012   ,  0.83485001,  0.70267999, -0.058322  ,
        0.70643997,  0.35979   , -0.48139   , -0.64253998,  0.21908   ,
       -0.27849001, -0.22995999, -0.032314  , -0.12598   , -0.28935   ,
        0.059438  ,  0.36079001, -0.23272   ,  0.16929001,  0.091287  ,
       -0.44764999,  0.30562001, -0.10323   ,  0.47902   , -0.031699  ,
       -0.1009    ,  0.20048   ,  0.83442998,  0.57710999,  0.66645002,
       -0.41485   , -0.37652001,  0.25514999, -0.24992999, -0.50915998,
       -0.35992   , -0.12351   ,  0.35596001,  0.37491   , -0.05548   ,
       -0.60108   ,  0.18746001,  0.13669001,  0.3044    ,  0.19981   ,
       -0.006172  , -0.50748003,  0.32308999,  0.26857001,  0.23422   ,
        0.099418  , -0.7281    , -0.32411   ,  0.53618997,  0.27300999,
        0.0012767 ,  0.084466  , -0.29696   ,  0.086806  , -0.29682001,
       -0.075324  , -0.31669   ,  0.19908001,  0.16267   ,  0.19402   ,
       -0.48322999,  0.22931001, -0.53293002, -0.64165002,  1.2579    ,
       -0.19943   ,  0.0081311 ,  0.41953999,  0.06253   ,  0.13387001,
       -0.27090999, -0.25729001, -0.12154   ,  0.22368   ,  0.1156    ,
       -0.56753999,  0.32565001, -0.19663   , -0.32797   , -0.061104  ,
        0.045471  , -0.36296999, -0.46709001, -0.041339  , -0.10877   ,
       -0.31126001,  0.088284  , -0.11884   ,  0.12775999,  0.2278    ,
       -0.15277   ,  0.11224   ,  0.2133    , -0.31865001, -0.22328   ,
       -0.59697002,  0.31641999, -0.24216001,  0.22971   ,  0.75164002,
       -0.19707   ,  0.11764   ,  0.097696  , -0.0091149 ,  0.43522999,
       -0.12136   , -0.37164   ,  0.019332  , -0.45868   , -0.60272998,
       -0.17852999,  0.33972001,  0.03559   ,  0.48407   ,  0.26058999,
        0.25753   , -0.25154999, -0.037034  , -0.17206   ,  0.55895001,
        0.02566   ,  0.34896001,  0.058973  , -0.58016998, -0.43430001,
        0.076113  , -0.065514  ,  0.43768999, -0.89503998, -0.43482   ,
       -0.053899  , -0.35696   , -0.14057   , -0.069972  , -0.039529  ,
        0.12328   , -0.19384   ,  0.50265002,  0.20554   ,  0.040245  ,
        0.022202  ,  0.017232  ,  0.38332999,  0.10679   ,  0.26583001,
       -0.24022999,  0.0096255 , -0.11114   ,  0.099512  , -0.03949   ,
        0.25139001, -0.30199999,  0.37564999,  0.12596001, -0.11147   ,
       -0.36769   ,  0.083935  , -0.02848   , -0.44128001,  0.26440999,
       -0.36115   , -0.38949001,  0.07962   , -0.62943   ,  0.36827001,
        0.0066196 ,  0.17067   ,  0.16759001,  0.63235998,  0.22036999,
       -0.2467    , -0.20492999, -0.55612999,  0.013659  , -0.35642001])

Finally, you can create a new model and load the pre-trained word embedding matrix into the Embedding layer.

You must complete the code for the create_model_with_embeddings function. This function takes as input the size of the vocabulary, the number of labels, the maxlen, embedding_dim and rnn_units hyperparameters, and the embedding matrix created by create_embedding_matrix. The function should construct, compile and return a RNN equal to the one built in create_model with the only difference that the Embedding layer must be initialized with the embedding matrix. Use the Constant initializer for this purpose. For this task, the Embedding layer must be kept trainable so the embeddings can be updated during training.

The configuration of the Embedding layer for this version of the RNN should look like:

{'name': 'embedding_1',
 'trainable': True,
 'batch_input_shape': (None, 130),
 'dtype': 'float32',
 'input_dim': 5508,
 'output_dim': 300,
 'embeddings_initializer': {'class_name': 'Constant',
  'config': {'value': array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,
            0.        ,  0.        ],
          [ 0.        ,  0.        ,  0.        , ...,  0.        ,
            0.        ,  0.        ],
          [ 0.72004002,  0.80954999,  0.77170002, ...,  0.39351001,
           -0.47082999, -0.60759002],
          ...,
          [ 0.        ,  0.        ,  0.        , ...,  0.        ,
            0.        ,  0.        ],
          [-0.40123999, -0.27991   , -0.42445999, ...,  0.45576   ,
            0.61864001, -0.30489001],
          [ 0.        ,  0.        ,  0.        , ...,  0.        ,
            0.        ,  0.        ]])}},
 'embeddings_regularizer': None,
 'activity_regularizer': None,
 'embeddings_constraint': None,
 'mask_zero': True,
 'input_length': 130}

test_create_model_with_embeddings= 2 Marks

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense, TimeDistributed
from tensorflow.keras.initializers import Constant

def create_model_with_embeddings(vocab_size, label_size, maxlen, embedding_dim, rnn_units, embedding_matrix):
    model = Sequential()
    model.add(Embedding(input_dim=vocab_size,
                        output_dim=embedding_dim,
                        embeddings_initializer=Constant(embedding_matrix),
                        input_length=maxlen,  # Ensure input_length is set correctly
                        trainable=True,
                        mask_zero=True))
    model.add(Bidirectional(LSTM(rnn_units, return_sequences=True)))
    model.add(TimeDistributed(Dense(label_size, activation='softmax')))
    
    model.compile(loss='sparse_categorical_crossentropy',
                  optimizer='adam',
                  metrics=['sparse_categorical_accuracy'])
    
    return model

model_with_embeddings = create_model_with_embeddings(vocab_size, label_size, maxlen, embedding_dim, rnn_units, embedding_matrix)
model_with_embeddings.get_layer(index=0).get_config()

C:\Users\Vishal\anaconda3\Lib\site-packages\keras\src\layers\core\embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.
  warnings.warn(

{'name': 'embedding_1',
 'trainable': True,
 'dtype': 'float32',
 'input_dim': 5508,
 'output_dim': 300,
 'embeddings_initializer': {'module': 'keras.initializers',
  'class_name': 'Constant',
  'config': {'value': {'class_name': '__numpy__',
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  'registered_name': None},
 'embeddings_regularizer': None,
 'activity_regularizer': None,
 'embeddings_constraint': None,
 'mask_zero': True}

Initializing the Embedding layer with GloVe embeddings should have a positive impact on the model performance for Quantity recognition by improving the recall.

              precision    recall  f1-score   support

    Quantity       0.85      0.79      0.82      1263

   micro avg       0.85      0.79      0.82      1263
   macro avg       0.85      0.79      0.82      1263
weighted avg       0.85      0.79      0.82      1263

train_model(model_with_embeddings, x_train, y_train, x_dev, y_dev, batch_size, epochs)

Epoch 1/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 49s 1s/step - loss: 0.4824 - sparse_categorical_accuracy: 0.3160 - val_loss: 0.1767 - val_sparse_categorical_accuracy: 0.2162
Epoch 2/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 29s 1s/step - loss: 0.1463 - sparse_categorical_accuracy: 0.2075 - val_loss: 0.1207 - val_sparse_categorical_accuracy: 0.2190
Epoch 3/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 34s 2s/step - loss: 0.0919 - sparse_categorical_accuracy: 0.2109 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.2203
Epoch 4/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 33s 2s/step - loss: 0.0648 - sparse_categorical_accuracy: 0.2129 - val_loss: 0.0847 - val_sparse_categorical_accuracy: 0.2214
Epoch 5/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 32s 1s/step - loss: 0.0467 - sparse_categorical_accuracy: 0.2141 - val_loss: 0.0808 - val_sparse_categorical_accuracy: 0.2219
Epoch 6/6
22/22 ━━━━━━━━━━━━━━━━━━━━ 31s 1s/step - loss: 0.0344 - sparse_categorical_accuracy: 0.2150 - val_loss: 0.0796 - val_sparse_categorical_accuracy: 0.2221

<keras.src.callbacks.history.History at 0x20158ea2b50>

predictions_with_embeddings = make_predictions(model_with_embeddings, x_test, batch_size)
test_data['prediction'] = predictions_to_labels(predictions_with_embeddings, x_test, labels)

1/1 ━━━━━━━━━━━━━━━━━━━━ 5s 5s/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 275ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 268ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 269ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 3s 3s/step

evaluate(test_data)

              precision    recall  f1-score   support

    Quantity       0.92      0.74      0.82      1263

   micro avg       0.92      0.74      0.82      1263
   macro avg       0.92      0.74      0.82      1263
weighted avg       0.92      0.74      0.82      1263